no code implementations • 17 Apr 2022 • D. M. Anisuzzaman, Yash Patel, Jeffrey Niezgoda, Sandeep Gopalakrishnan, Zeyun Yu
This study used wound photos to construct a deep neural network-based wound severity classifier that classified them into one of three classes: green, yellow, or red.
no code implementations • 14 Sep 2021 • D. M. Anisuzzaman, Yash Patel, Behrouz Rostami, Jeffrey Niezgoda, Sandeep Gopalakrishnan, Zeyun Yu
This study developed a deep neural network-based multi-modal classifier using wound images and their corresponding locations to categorize wound images into multiple classes, including diabetic, pressure, surgical, and venous ulcers.
no code implementations • 3 May 2021 • Farnaz H. Foomani, D. M. Anisuzzaman, Jeffrey Niezgoda, Jonathan Niezgoda, William Guns, Sandeep Gopalakrishnan, Zeyun Yu
We utilized samples generated by our proposed GAN in training a prognosis model to demonstrate its real-life application.
1 code implementation • 19 Oct 2020 • Behrouz Rostami, D. M. Anisuzzaman, Chuanbo Wang, Sandeep Gopalakrishnan, Jeffrey Niezgoda, Zeyun Yu
Acute and chronic wounds are a challenge to healthcare systems around the world and affect many people's lives annually.
no code implementations • 15 Sep 2020 • D. M. Anisuzzaman, Chuanbo Wang, Behrouz Rostami, Sandeep Gopalakrishnan, Jeffrey Niezgoda, Zeyun Yu
Efficient and effective assessment of acute and chronic wounds can help wound care teams in clinical practice to greatly improve wound diagnosis, optimize treatment plans, ease the workload and achieve health related quality of life to the patient population.
1 code implementation • 15 Sep 2020 • D. M. Anisuzzaman, Yash Patel, Jeffrey Niezgoda, Sandeep Gopalakrishnan, Zeyun Yu
We present an automated wound localizer from 2D wound and ulcer images by using deep neural network, as the first step towards building an automated and complete wound diagnostic system.